新水平集方法与以往体图像分割方法的性能比较

Myungeun Lee, Wanhyun Cho, Sun-Worl Kim, Yanjuan Chen, Soohyung Kim
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引用次数: 0

摘要

在本文中,我们将所提出的方法与先前的基于水平集的体积图像分割方法进行了比较。为了获得精确的分割,我们提出的速度函数中使用了图像对象的区域和边界信息。边界信息由梯度图像得到的梯度向量流定义,区域信息由感兴趣区域像素强度的高斯分布信息定义,用于图像分割。同时利用正则项去除表面周围的噪声。我们给出了各种真实医学体图像的实验结果,以验证所提出方法的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Comparison Between New Level Set Method and Previous Methods for Volume Images Segmentation
In this paper, we compare our proposed method with previous methods for the volumetric image segmentation using level set. In order to obtain an exact segmentation, the region and boundary information of image object are used in our proposed speed function. The boundary information is defined by the gradient vector flow obtained from the gradient images and the region information is defined by Gaussian distribution information of pixel intensity in a region-of-interest for image segmentation. Also the regular term is used to remove the noise around surface. We show various experimental results of real medical volume images to verify the superiority of proposed method.
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